Federated Gaussian Process Learning via Pseudo-Representations for Large-Scale Multi-Robot Systems

Multi-robot systems require scalable and federated methods to modelcomplexenvironmentsundercomputationalandcommunication constraints. Gaussian Processes (GPs) offer robust probabilistic modeling, but suffer from cubic computational complexity, limiting their applicability in large-scale deployments. To address this challenge, we introduce the pxpGP, a novel distributed GP framework tailored for both centralized and decentralized large-scale multi-robot networks. Our approach leverages sparse variational inference to generate a local compact pseudo-representation. We introduce a sparse variational optimization scheme that bounds local pseudo-datasets and formulate a global scaled proximal-inexact consensus alternating direction method of multipliers (ADMM) with adaptive parameter updates and warm-start initialization. Experiments on synthetic and real-world datasets demonstrate that pxpGP and its decentralized variant, dec-pxpGP, outperform existing distributed GP methods in hyperparameter estimation and prediction accuracy, particularly in large-scale networks.

Authors

• Sanket A. Salunkhe Colorado School of Mines
• George P. Kontoudis Colorado School of Mines

Keywords

• GaussianProcesses
• Multi-RobotSystems
• DistributedOptimization
• Sparse Methods
• Federated Learning
• Large-Scale Networks